Constant and variable returns to scale DEA models for socially responsible investment funds

In order to evaluate the performance of socially responsible investment (SRI) funds, we propose some models which use data envelopment analysis (DEA) and can be computed in all phases of the business cycle. These models focus on the most crucial elements of an investment in mutual funds.     

Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance.     

Socially Responsible Investment: A multicriteria approach to portfolio selection combining ethical and financial objectives

In a context of Socially Responsible Investment (SRI), this paper deals with portfolio selection for investors interested in ethical policies. In the opportunity set there are ethical assets and other assets which are not characterized as ethical. Two goals are considered, the traditional financial goal in the classical utility theory under uncertainty and an ethical goal in the same utility framework. A new financial-ethical bi-criteria model is proposed with absolute risk aversion coefficients and targets depending on the investor’s ethical profile. This approach is relevant as an increasing number of mutual funds are becoming interested in SRI strategies. From the proposed model, an actual case on green investment is developed. Concerning this case (without generalizing to other contexts), an analysis of the numerical results shows that efficient portfolios obtained by the traditional E-V model outperform the strong green portfolios in terms of expected return and risk, but this does not significantly occur with weak green investment.     

基于最小凸输入需求集方法的基金业绩评价

本文首先回顾了传统基金业绩评价方法，然后运用Chang（1999）的最小凸输入需求集（MCIRS）方法对我国2000年前上市的20只封闭式证券投资基金在2000年的业绩进行综合评价。实证研究表明：同益、景博、汉盛、金鑫、兴和、泰和、天元、普丰共8只基金相对有效，而其余12只基金则相对无效。并将结果与数据包络分析（DEA）的结果进行了比较。     

组合DEA方法与成熟度模型对项目效益的评价

为全面考虑资金、管理决策能力等因素对项目效益的影响，本运用数据包络分析与项目成熟度模型结合的方法来对企业各个项目之间的相对效益进行评价，应用结果表明该评价方法对于企业资源的最优配置、提高总体效益是十分有用的。     

The sustainability balanced scorecard as a framework for selecting socially responsible investment: an effective MCDM model

In recent years, sustainable development and social responsibility have become important issues around the globe. Investors are interested in the so-called ‘socially responsible investment’ (SRI), an investment strategy that employs criteria other than financial risk and return when selecting firms in which to invest. The practice of SRI shows that there are growing numbers of investors who want to take account of more criteria. Given this worldwide trend, the question arises how these issues can be incorporated into the investment decision process. This paper proposes a novel integrated model for selecting SRI stocks and illustrates the practical application of such a model through a case study. This model first applied the decision making trial and evaluation laboratory (DEMATEL) approach to deal with the interdependencies existing among the criteria of organization requires, and then integrated the DEMATEL, the analytic network process, and the zero-one goal programming method to select an optimal portfolio of SRI. Additionally, we used the sustainability balanced scorecard as a multi-criteria framework for SRI evaluation. This integrated model enables the management to obtain the fitting SRI portfolio and achieves the desired spiritual value.     

Can asset allocation limits determine portfolio risk–return profiles in DC pension schemes?

In defined contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk–return profiles. In this article, we challenge this approach and show that such funds can exhibit erratic risk–return profiles that deviate significantly from the intended design. We propose to replace all minimum and maximum asset allocation constraints by a single risk metric (or measure) that controls risk directly. Thus, funds with different risk–return profiles can be immediately created by adjusting the risk tolerance parameter accordingly. Using data from the Chilean DC pension system, we show that our approach generates funds whose risk–return profiles are consistently ordered according to the intended design, and outperforms funds created by means of asset allocation limits.     

A robust nonparametric approach to evaluate and explain the performance of mutual funds

The topic of the measurement of mutual funds’ performance is receiving an increasing interest both from an applied and a theoretical perspective. Beside the traditional financial literature, a growing body of studies has started to apply the tools of frontier analysis for benchmarking comparisons in portfolio analysis. Our paper contributes to this literature proposing a robust nonparametric approach for analysing mutual funds. It is based on the concept of order-m frontier [Cazals, C., Florens, J.P., Simar, L., 2002. Nonparametric frontier estimation: A robust approach. Journal of Econometrics 106, 1–25] and on a probabilistic approach [Daraio, C., Simar, L., 2005. Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis 24 (1), 93–121] to find out the factors explaining mutual funds’ performance. Within this framework, a decomposition of conditional efficiency is proposed, and its usefulness for economic interpretation analysed. Our approach is illustrated by using US mutual funds data, grouped for category by objective. Economies of scale, slacks and market risks are investigated. A comparison of traditional, nonparametric and robust performance measures is also offered.     

Mean–variance optimal portfolios in the presence of a benchmark with applications to fraud detection

We first study mean–variance efficient portfolios when there are no trading constraints and show that optimal strategies perform poorly in bear markets. We then assume that investors use a stochastic benchmark (linked to the market) as a reference portfolio. We derive mean–variance efficient portfolios when investors aim to achieve a given correlation (or a given dependence structure) with this benchmark. We also provide upper bounds on Sharpe ratios and show how these bounds can be useful for fraud detection. For example, it is shown that under some conditions it is not possible for investment funds to display a negative correlation with the financial market and to have a positive Sharpe ratio. All the results are illustrated in a Black–Scholes market.     

Fuzzy risk adjusted performance measures: Application to hedge funds

In this paper, following the notion of probabilistic risk adjusted performance measures, we introduce that of fuzzy risk adjusted performance measures (FRAPM). In order to deal efficiently with the closing-based returns bias induced by market microstructure noise, as well as to handle their uncertain variability, we combine fuzzy set theory and probability theory. The returns are first represented as fuzzy random variables and then used in defining fuzzy versions of some adjusted performance measures. Using a recent ordering method for fuzzy numbers, we propose a ranking of funds based on these fuzzy performance measures. Finally, empirical studies carried out on fifty French hedge funds confirm the effectiveness and give the benefits of our approach over the classical performance ratios.     

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if–then rules.     

MANAGEMENT STRATEGY EVALUATION OF PHEROMONE‐BAITED TRAPPING TECHNIQUES TO IMPROVE MANAGEMENT OF INVASIVE SEA LAMPREY

We applied a management strategy evaluation (MSE) model to examine the potential cost‐effectiveness of using pheromone‐baited trapping along with conventional lampricide treatment to manage invasive sea lamprey. Four pheromone‐baited trapping strategies were modeled: (1) stream activation wherein pheromone was applied to existing traps to achieve 10^?12 mol/L in‐stream concentration, (2) stream activation plus two additional traps downstream with pheromone applied at 2.5 mg/hr (reverse‐intercept approach), (3) trap activation wherein pheromone was applied at 10 mg/hr to existing traps, and (4) trap activation and reverse‐intercept approach. Each new strategy was applied, with remaining funds applied to conventional lampricide control. Simulating deployment of these hybrid strategies on fourteen Lake Michigan streams resulted in increases of 17 and 11% (strategies 1 and 2) and decreases of 4 and 7% (strategies 3 and 4) of the lakewide mean abundance of adult sea lamprey relative to status quo. MSE revealed performance targets for trap efficacy to guide additional research because results indicate that combining lampricides and high efficacy trapping technologies can reduce sea lamprey abundance on average without increasing control costs.     

基于行为金融视角的基金申赎行为实证研究

以开放式证券投资基金申购、赎回行为背后的行为金融机理为研究对象,通过分析相关变量作用于基金投资者心理的方式和路径,探讨其对基金申购、赎回所产生的影响。在理论分析的基础上,提出基金业绩、规模、存续时间、价位、分红等变量能够各自基于信号传递、心理账户、预期框定偏差、预期惯性、处置效应等多元化路径影响投资者心理,并作用于基金申购、赎回和基金流量,并据此提出了相关假设。进一步地,通过建立结构方程模型,以我国327只股票型开放式证券投资基金为样本,对2011至2013年三组年度截面数据进行了检验。实证结果表明信号传递、预期框定偏差路径始终稳定存在,预期惯性路径仅在2011、2013年实证检验中存在,处置效应路径只在2012年检验中存在、且呈现非对称性特征,而心理账户路径则无法得到证实,据此得出结论认为基金业绩对基金申赎影响最为显著但不具稳定性。最后,从投资者行为模式和基金市场发展环境等角度对实证结果进行了分析。     

A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.     

Hedge fund performance appraisal using data envelopment analysis

In this paper we apply data envelopment analysis (DEA) to evaluate the performance of hedge fund classifications. The purpose of alternative investment strategies such as hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance could be ineffective. With the increasing number of hedge funds available, institutional investors, pension funds, and high net worth individuals urgently need a trustworthy efficiency appraisal method. DEA can achieve this. An important benefit of the DEA measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal distribution of hedge fund returns. We suggest that DEA be used as a complimentary technique (or method) for the selection of efficient hedge funds and funds of hedge funds for investors. Using DEA can shed light and further validate hedge fund manager selection with other methodologies.     

Study on the coordination contract in supply chain under trade credit based on risk compensation

Trade credit changes the inventory risk between supplier and retailer. This leads to failure in the coordination of the supply chain. Considering that the supplier bears the retailer’s inventory risk under the credit condition, in this paper, the contract is constructed by combining the risk compensation and quantity discount contract to re-coordinate the supply chain and analyze the contract. The results show that the contract can achieve voluntary supply chain coordination; and when the seller’s funds is within a certain range, the coordinate contract can perform in the form of the wholesale price contract, and the wholesale price is influenced by its own funds and product value. In the end, a numerical example is given to verify this conclusion.     

A benchmarking approach to optimal asset allocation for insurers and pension funds

We solve the optimal asset allocation problem for an insurer or pension fund by using a benchmarking approach. Under this approach the objective is an increasing function of the relative performance of the asset portfolio compared to a benchmark. The benchmark can be, for example, a function of an insurer’s liability payments, or the (either contractual or target) payments of a pension fund. The benchmarking approach tolerates but progressively penalizes shortfalls, while at the same time progressively rewards outperformance. Working in a general, possibly non-Markovian setting, a solution to the optimization problem is presented, providing insights into the impact of benchmarking on the resulting optimal portfolio. We further illustrate the results with a detailed example involving an option based benchmark of particular interest to insurers and pension funds, and present closed form solutions.     

Performance predictions in solid oxide fuel cells

The results of numerical calculations performed for planar solid oxide fuel cells are presented. Two different approaches are developed: (i) A detail numerical method and (ii) a presumed flow method. In the first approach, a commercial computational fluid dynamics code is employed, and user-defined-functions are developed to account for electro-chemical considerations. In the second approach, where the momentum equations do not require to be solved, an in-house code is developed and used to perform calculations. In both cases the following coupled physicochemical phenomena are modelled; heat and mass transfer, electrochemistry and electric potential. The polarisation curve is generally accepted as an important performance measure of the fuel cell. Performance predictions for this characteristic made by the two different approaches are compared. Results show voltage losses due activation, Ohmic resistance, and mass transfer in a typical solid oxide fuel cell, over a range of current density values. The results for the detailed numerical method are discussed in some detail with regard to the influence of different parameters on the overall performance of the device.     

Fitness Landscape Analysis for Scalable Multicast RRM Problem in Cellular Network

This paper aims to solve the Radio Resource Management (RRM) problem for Multimedia Broadcast Multicast Service (MBMS) system in cellular network. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristic approach. We conduct fitness landscape analysis to study the characteristics of the proposed model, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms.